The iPlant Collaborative Community Cyberinfrastructure for Life Science Tools and Services Workshop GWAS/QTL Apps Overview

Advantages Population type, phenotype data, environments, size of dataset… Options to suit your needs All the compute power you need—with a million snps you may need more, with many phenotypes you may need more Atmosphere for testing, analyses that run on few resources, and visualization Agave API installs for very large data analyses, will be able to expose as DE app Discovery Environment for apps that are likely to be used often and don’t require too many resources

Consideration: Population type, phenotype data, environments, size of dataset Historical population requires fitting of population structure Use FaST-LMM, Structure then TASSEL, GEMMA Environments? Fit covariates as fixed with FaST-LMM, GEMMA, fit as random with QXPak, R packages such as lme4, aml adaptive lasso Already in Atmosphere Already in Discovery Environment Predictions—use GenSel app, BATools

Some applications will be easier than others to use and install Many methods out there If installed already—just figure out which parameters you want to set Installing an app Aaron will talk about this more tomorrow (use known-truth simulations to see what works) Check that the parameters you need are visible Will it in run in your Atmosphere allocation? Install and do a test run! If not, install via Agave API --ask for help as needed, iPlant can provide software engineering and install support via Extended Collaborative Support form and short review process

What do you need to do your own g2p data analysis? Input data (BTW, GBS tools are available to get snps, BISQUE to help get phenotype values from images) File-format conversion—you may need to write a script if your dataset is very large Suitable analysis application for genotype-phenotype association—can be a pain to match your needs to what is out there, get statistical help Visualization of output—may need to use graphics package in Atmosphere or a commercial one that you like on your own computer

Some applications will be easier than others to use and install… Open source best Computationally efficient (C, fortran) if possible Large user community, developers available

Success stories: API used to associate g2p: very large number of metabolites measured in a very large number of genotypes iPlant staff assisted GenSel installed by developers, made available through the DE For whole-genome predictions, widely used in breeding

Keep asking: ask.iplantcollabortive.org

The iPlant Collaborative is funded by a grant from the National Science Foundation Plant Cyberinfrastructure Program (#DBI ).